FACE-Q Craniofacial Module: Part 1 validation of CLEFT-Q scales for use in children and young adults with facial conditions
Bibliographic record
Abstract
BACKGROUND: The CLEFT-Q includes 12 independently functioning scales that measure appearance (face, nose, nostrils, teeth, lips, jaws), health-related quality of life (psychological, social, school, speech distress), and speech function, and an eating/drinking checklist. Previous qualitative research revealed that the CLEFT-Q has content validity in noncleft craniofacial conditions. This study aimed to examine the psychometric performance of the CLEFT-Q in an international sample of patients with a broad range of facial conditions. METHODS: Data were collected between October 2016 and December 2019 from 2132 patients aged 8 to 29 years with noncleft facial conditions. Rasch measurement theory (RMT) analysis was used to examine Differential Item Function (DIF) by comparing the original CLEFT-Q sample and the new FACE-Q craniofacial sample. Reliability and validity of the scales in a combined cleft and craniofacial sample (n=4743) were examined. RESULTS: DIF was found for 23 CLEFT-Q items when the datasets for the two samples were compared. When items with DIF were split by sample, correlations between the original and split person locations showed that DIF had negligible impact on scale scoring (correlations ≥0.995). In the combined sample, RMT analysis led to the retention of original content for ten CLEFT-Q scales, modification of the Teeth scale, and the addition of an Eating/Drinking scale. Data obtained fit with the Rasch model for 11 scales (exception School, p=0.04). Person Separation Index and Cronbach alpha values met the criteria. CONCLUSION: The scales described in this study can be used to measure outcomes in children and young adults with cleft and noncleft craniofacial conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".